Stock market prediction is one of the most difficult computations due to the many internal as well as any number and type of external factors. It is impossible to get the exact computation hence we look for the method which gives the computation with less error. Different machine learning methods are being applied for the computations which involve many parameters. In this research work we choose Long Short-Term Memory (LSTM) for the prediction as it is computationally suitable for these types of data analysis. After doing the prediction of share price the work is extended to manage portfolio of the mutual fund. The framework has been designed in such a way so that the portfolio manager can choose any number of business sectors as well as any number of shares belong to this sector. This research work henceforth applicable for computing individual share price as well as managing a diversified portfolio. © 2020 ISCA.